As technology permeates day-to-day life, people have more and more ways to communicate. These forms of communication create a challenge for Public Safety Answering Points (PSAP). Research has shown people are posting on social media for medical help, but PSAPs do not have a way to receive these messages. This research aims to determine if using keywords and filter words can be used to find the actionable calls for help in the midst of the millions of posts made. Actionable is defined as containing enough information to determine the nature of a medical emergency and if is currently occurring or is recent enough that the poster needs help. To determine if this was true, the most prevalent types of voice medical calls to the Cincinnati Fire Department were determined for 2021. A set of keywords and filter words was created for each call type. Then tweets were captured over a period of seventeen hours and filtered using the word lists. The filtering showed there was a valid way to find actionable tweets, and that people were posting such things. By varying the word lists, the signal-to-noise ratio can be adjusted depending on the desires of the agency. as filtering became more strict, the number of missed actionable tweets increased, while the number of incorrectly labeled as actionable decreased.
This microsatellite dataset was constructed using eight microsatellite loci with 270 individual samples, representing wild population of Euonymus fortunei in Ohio, Kentucky, Kansas, and Minnesota. Also represented are multiple individuals from several Euonymus cultivars and also wild E. alatus (burning bush) from Ohio. This database is published as Elam RJ and Culley TM (2023) Genetic Analysis of Invasive Spread of Euonymus fortunei (Wintercreeper), a Popular Ornamental Groundcover. Invasive Plant Science and Management.
Replication Package of Environmental Variations of Software Features: A Logical Test Cases' Perspective authored by Md Rayhan Amin,Tanmay Bhowmik, Nan Niu, and Juha Savolainen
Artifacts of the paper entitled:
Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning
Authors: Hemanth Gudaparthi, Nan Niu, Boyang Wang, Tanmay Bhowmik, Hui Liu, Jianzhang Zhang, Juha Savolainen, Glen Horton, Sean Crowe, Thomas Scherz and Lisa Haitz
To appear in the Proceedings of the 31st IEEE International Requirements Engineering Conference (RE 2023 https://conf.researchr.org/home/RE-2023)
